Structural and functional alterations in the brain gray matter among first-degree relatives of

schizophrenia patients: a multimodal meta-analysis of fMRI and VBM studies

Running title: Familial risk for schizophrenia and alterations in the brain

Aino I. L. Saarinen, PhD1,2,3,*, Sanna Huhtaniska, MD, PhD4, Juho Pudas, MB3, Lassi Björnholm, MD3,

Tuomas Jukuri, MD, PhD3, Jussi Tohka, MD, PhD5, Niklas Granö, PhD6, Jennifer H. Barnett, PhD7,8,

Vesa Kiviniemi, MD, PhD9,10, Juha Veijola, MD, PhD3,10,11, Mirka Hintsanen, PhD1,

Johannes Lieslehto, MD, PhD 12

1 Research Unit of Psychology, University of Oulu, Finland

2 Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland

3 Research Unit of Clinical Neuroscience, Department of Psychiatry, University of Oulu, Finland

4 Center for Life Course Health Research, University of Oulu, Finland

5 A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

6 Helsinki University Hospital, Department of Adolescent Psychiatry, Finland

7 Cambridge Cognition, Cambridge, UK

8 Department of Psychiatry, University of Cambridge, Cambridge, UK

9 Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland

10 Department of Psychiatry, Oulu University Hospital, Oulu, Finland

11 Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu,

Finland

12 Section for Neurodiagnostic Applications, Department of Psychiatry, Ludwig Maximilian University,

Nussbaumstrasse 7, 80336 Munich, Bavaria,

* Corresponding author: Aino Saarinen. Department of Psychology and Logopedics, Faculty of Medicine,

Haartmaninkatu 3, P.O. Box 21, 00014 University of Helsinki, Finland. E-mail: [email protected],

Tel.: +35844 307 1204.

1

Abstract

Objective: Schizophrenia has one of the highest heritability estimates in psychiatry, but the genetically- based underlying neuropathology has mainly remained unclear. We conducted a multimodal coordinate- based meta-analysis (CBMA) to investigate brain structural and functional alterations in individuals with high familial risk for schizophrenia, i.e. in first-degree relatives of schizophrenia patients (FRs). Methods:

We conducted a systematic literature search from two electronic databases to find studies that examined differences between FRs and healthy controls using whole-brain functional magnetic resonance imaging

(fMRI) or voxel-based morphometry (VBM). A CBMA of 30 fMRI (754 FRs; 959 controls) and 11 VBM

(885 FRs; 775 controls) datasets were conducted using the anisotropic effect-size version of signed differential mapping. Further, we conducted separate meta-analyses about functional alterations in different cognitive tasks: social cognition, executive functioning, working memory, and inhibitory control. Results:

When compared to healthy controls, FRs showed higher fMRI activation in the right frontal gyrus during cognitive tasks. In VBM studies, there were no differences in grey matter density between FRs and healthy controls. Furthermore, multi-modal meta-analysis obtained no differences between FRs and healthy controls. Finally, by utilizing the BrainMap database, we showed that the brain region which showed functional alterations in FRs (i) overlapped only slightly with the brain regions that were affected in the meta-analysis of schizophrenia patients and (ii) correlated positively with the brain regions that exhibited increased activity during cognitive tasks in healthy individuals. Conclusions: Based on this meta-analysis,

FRs may exhibit only minor functional alterations in the brain during cognitive tasks, and the alterations are much more restricted and only slightly overlapping with the regions that are affected in schizophrenia patients. The familial risk did not relate to structural alterations in the grey matter.

Keywords: Schizophrenia; Psychosis; Genetic risk; Familial risk; Brain structure; Brain activity

2

1 Introduction

The heritability of schizophrenia and psychotic disorders may be as high as 80% (Sullivan et al., 2003), but the genetically-based underlying neuropathology is mostly unknown. First-degree relatives of schizophrenia patients (FRs) compose a particular risk group since their lifetime morbidity risk for schizophrenia is increased ten-fold to 10% (Gottesman et al., 2010; Lichtenstein et al., 2006). Consequently, when evaluating an individual’s risk for schizophrenia, the familial risk is among the most important factors (Mäki et al.,

2005).

Cognitive impairment is very common in prodromal schizophrenia (Cornblatt et al., 2004;

Lencz et al., 2006). Furthermore, large cognitive deficits are present prior to illness onset and represent vulnerability markers for the onset of the disorder (Carrión et al., 2018). Along with this, cognitive impairment is also included as a diagnostic criterion for schizophrenia in the DSM-V classification. Overall, cognitive functioning is shown to be more severely impaired in genetic than clinical high-risk populations

(Seidman et al., 2010). The most affected cognitive abilities are executive functioning, such as working memory and inhibitory control (Snitz et al., 2005), and social cognition (Cornblatt et al., 2003; Hans et al.,

2010). Consequently, cognitive impairment is a crucial element when aiming to identify predictors for the onset of schizophrenia.

Previous meta-analyses suggest that relatives of schizophrenia patients have increased activity in the right-side parietal, temporal, and frontal regions (Cooper et al., 2014; Scognamiglio et al., 2014;

Zhang et al., 2016). Findings on brain regions with decreased activity in FRs have been inconclusive, with findings in the thalamus, cerebellum, cingulate, or frontal lobes (Cooper et al., 2014; Scognamiglio et al.,

2014; Zhang et al., 2016; Niu et al., 2017). Regarding structural alterations, FRs seem to have decreased gray matter in the insula and frontal regions, even though the findings have varied somewhat (Cooper et al.,

2014; Niu et al., 2017; Boos et al., 2007). Multimodal meta-analyses in FRs have been inconclusive (Cooper et al., 2014; Niu et al., 2017).

3 An updated meta-analysis on this topic is very much needed for several reasons. Firstly, a growing number of original studies have been conducted on this topic during recent years, so a higher number of participants are available for a meta-analysis. Secondly, previous meta-analyses have not investigated functional alterations in FRs separately in different cognitive tasks. This might be important since there is evidence that different cognitive abilities may be selectively impaired among individuals at risk for schizophrenia (Cornblatt et al., 2003; Hans et al., 2000). Thirdly, there is a possibility that alterations in genetic high-risk individuals are located in overlapping regions but are more subtle than in schizophrenia patients. However, this has remained uninvestigated in the previous meta-analyses. Finally, previous meta- analyses have not examined if the functional alterations during cognitive tasks in FRs are located in the brain regions that exhibit increased activity during cognitive tasks in healthy individuals. This is a crucial question since there is evidence about compensating mechanisms in the brain among individuals at risk for psychosis (Cooper et al., 2014; Fusar-Poli et al., 2010; Pulkkinen et al., 2015). For example, it is possible that before any psychotic symptomatology has emerged, FRs can compensate for mild cognitive impairments by activating more extensive brain networks (i.e. different/additional brain regions than in healthy individuals) during a cognitive task.

Our first aim was to conduct a multimodal meta-analysis in order to investigate the functional and structural alterations in first-degree relatives of schizophrenia patients. We included peer-reviewed functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) studies with whole- brain scanning. Our second aim was to investigate whether brain regions with structural or functional alterations in FRs overlap with the regions (i) that are affected in schizophrenia patients or (ii) that exhibit increased activity during cognitive tasks in healthy individuals. For this purpose, we utilized the publically available meta-analysis database BrainMap. We did not set a-priori hypotheses in this study.

2 Methods

2.1 Search strategies

4 The MOOSE (Meta-analyses Of Observational Studies in Epidemiology) Checklist was followed throughout the meta-analysis (https://www.elsevier.com/__data/promis_misc/ISSM_MOOSE_Checklist.pdf). The fulfilled form of the MOOSE Checklist can be found in Supplementary Table 1. A systematic literature search was carried out between August and November 2018 using the electronic databases of PubMed and

Web of Science. For fMRI studies, we used the following search terms: “schizophrenia” AND (“genetic risk” OR “familial risk” OR “parental risk” OR “relatives” OR “twins” OR “offspring” OR “siblings”) AND

(“fMRI” OR “functional MRI” OR “BOLD”). For VBM studies, the search terms included: “schizophrenia”

AND (“genetic risk” OR “familial risk” OR “parental risk” OR “relatives” OR “twins” OR “offspring” OR

“siblings”) AND (“VBM” OR “gray matter” OR “gray matter" OR "voxel-based morphometry"). There were no restrictions regarding language, publication date, or publication status, and the search was directed to all fields.

After removing duplicates, all identified studies were screened based on the title and abstract and defined as eligible/ineligible for the meta-analysis. In addition to original research papers, all meta- analyses and reviews identified by our search strategies were scrutinized, and their reference lists were manually checked for any additional eligible studies. After the abstract and title review, the identified full- text articles were screened more precisely on the basis of the exclusion and inclusion criteria (described in the next section). The article selection process is illustrated in Figure 1. In each phase of the article selection process, the eligibility of the inclusion/exclusion was double-checked by another author

(A.S./S.H./J.P./L.B./J.L.). The primary reasons for excluding articles are shown in Supplementary Tables 2 and 3.

2.2 Inclusion and exclusion criteria

The inclusion criteria for the identified studies were: a peer-reviewed original article; the study included fMRI or VBM on gray matter; subjects were first-degree relatives of schizophrenia patients; subjects were compared to a healthy control group; whole-brain scanning; T or Z statistics of the observed BOLD response difference between FRs and healthy controls were available; p-values were available; the coordinates were reported using the Talairach Atlas (Tal) or the Montreal Neurological Institute (MNI)

5 space. The exclusion criteria for the identified studies were as follows: only regions of interest (ROIs) were investigated; a small volume correction (SVC) was used; participants consisted exclusively of individuals with 22q11.2 deletion; the participants with familial risk for schizophrenia expressed psychotic symptomatology or had antipsychotic medications; the group size of the participants with familial risk for schizophrenia was less than 10; or a larger sample of the same population was provided in another included study. We also excluded studies that reported only functional connectivity-based group differences due to the significant variations in these techniques.

2.3 Data collection from the included studies

We collected the following information (if available): publication year, sample size, gender distribution, age, the score of the Global Assessment of Functioning (GAF), intelligence quotient (IQ), the diagnostic classification system that was used for the identification of schizophrenia, smoothing kernel (mm), psychopharmacological treatment of FRs (other than antipsychotic medications), mental disorders of FRs

(other than psychotic disorders), used analyzing software package for fMRI/VBM, magnetic field strength

(Tesla), the use of correction for multiple comparisons, and the use of covariates. Additionally, when applicable, we collected the x-, y- and z-coordinates (reported using Tal or MNI) of statistically significant findings and the direction of the observed difference between FRs and healthy controls. In case some necessary information was missing, we contacted the authors of the original articles.

2.4 Statistical analyses and meta-analytical models

We performed separate voxel-based meta-analyses of findings in fMRI activation, and regional gray matter volume (VBM) maps in individuals with FRs relative to healthy controls using an anisotropic effect-size version of signed differential mapping (AES-SDM v5.141, see http://www.sdmproject.com). The analytical processes of AES-SDM meta-analysis have been described in detail elsewhere (Radua et al., 2012b, 2014).

For the analysis, we extracted the peak coordinates and t-statistics of fMRI activation or gray matter differences between FRs and healthy controls from each included data set. We ensured that the same statistical threshold was used throughout the brain and throughout the study. If multiple thresholds were

6 used, we selected the most stringent threshold. If t-statistics were not available, we used the web-based tool provided by the AES-SDM (https://www.sdmproject.com/utilities/?show=Statistics) to convert z-statistics or p-values into t-statistics. Next, we estimated a standard MNI-map of fMRI activation or gray matter alterations (VBM) for each study separately using an anisotropic Gaussian kernel (full width at half maximum=20 mm). After that, we employed a random-effects model, taking into account the sample size, intra-study variance, and between-study heterogeneity. It has been demonstrated that high statistical stability can be acquired with 20 permutations (Radua et al., 2012b). To ensure the stability of the analyses, we performed these analyses using 50 permutations.

AES-SDM uses the following default statistical threshold: uncorrected voxel p-value of 0.005, peak height Z ≥ 1, and cluster extent ≥ 10 voxels. This thresholding approximates the corrected p-value of

0.05 and creates an optimal balance between sensitivity and specificity (Radua et al., 2012b). To avoid spurious findings, we set a more stringent threshold by using the significance level at the uncorrected voxel p-value of 0.0005, peak height Z = 2, and 80 voxels. The robustness of the results was assessed by 1) assessing the level of heterogeneity (using I2 statistics that refers to the percentage of total variance between studies resulting from rather a heterogeneity than chance); 2) inspecting the funnel plots for publication bias using Egger's test; and 3) implementing a jack-knife sensitivity analysis. Additionally, we conducted meta- regression analyses with age (in FR group), field strength, and sex distribution (in FR group) as a regressor

(using even more stringent threshold of p=0.0001).

Regarding fMRI studies, we conducted five separate analyses. The first analysis included all the fMRI studies (regardless of which cognitive tasks had been used). The second analysis included studies with executive functioning tasks. FMRI studies with executive functioning tasks were further classified into working memory tasks (Analysis 3) and inhibitory control tasks (Analysis 4). Analysis 5 included fMRI studies with social cognition tasks. The classification of cognitive tasks was based on the previous models of cognitive functions (Diamond et al., 2013; Miyake et al., 2012).

Finally, since we were interested in both functional and structural abnormalities in FRs, we also performed a multimodal analysis on the fMRI and VBM meta-analytical maps. The multimodal analysis was conducted according to Radua et al. (2013) to investigate potential conjunctions between the

7 VBM and fMRI modalities. Briefly, this method estimates the significance of the overlap between the actual p-values of the two modalities.

For non-neuroimaging statistical analyses, we used R (http://cran.r-project.org) version R 3.4.3

(R Core Team, 2014) with psych (Revelle, 2017) and metafor (Viechtbauer, 2010) packages. We conducted random effect models (visualized in forest plots) and analyzed the heterogeneity of the studies.

2.5 Comparison to the meta-analysis of previous fMRI and VBM studies in schizophrenia patients

As an additional analysis, we investigated whether the brain regions which exhibited functional or structural alterations in FRs in the meta-analysis overlapped with brain regions that are affected in schizophrenia patients. This investigation was conducted using the BrainMap database. The data collection methods of the

BrainMap database have been described with more detail elsewhere (Laird et al., 2011), and it has also been used in several previous meta-analyses (e.g. Daniel et al., 2016; Vanasse et al., 2018). Specifically, we conducted an additional automatic meta-analysis of the previous VBM and fMRI studies in schizophrenia patients (the search with Sleuth was conducted in August 2018, see http://www.brainmap.org/sleuth/). We identified 50 fMRI studies and 27 VBM studies. In the meta-analysis of fMRI studies, we used contrasts in both directions (i.e. schizophrenia>controls and schizophrenia

2.6 Comparison to the previous fMRI studies in healthy individuals

We investigated whether the brain regions that were found to be affected in FRs (in the fMRI and VBM meta-analysis) overlapped with the brain regions activated during behavioral tasks in healthy individuals.

First, we examined the brain activity maps during a wide variety of behavioral tasks (e.g. working memory,

8 language processing and emotion recognition.) in healthy individuals. This was done by using the BrainMap database (http://www.brainmap.org/taxonomy/behaviors.html) and conducting meta-analyses on the previous fMRI studies in healthy individuals during different behavioral tasks. We included all the behavioral domains that had been investigated in at least 17 previous fMRI studies, as this is suggested to be the minimum number of studies for running a meta-analysis on GingerAle (Eickhoff et al., 2016). Using this criterion, we retained 47 behavioral domains that are listed in Supplementary Table 4. Thereafter, we extracted Z-statistics of the unthresholded activity maps of each behavioral domain using the Automated

Anatomical Labeling (AAL) parcellation (Tzourio-Mazoyer et al., 2002). Next, we employed principal component analysis (PCA) on the 47 behavioral domains to reduce the dimensionality of the domains (using psych package in R). Behavioral domains that had a loading greater than 0.5 were considered as primary indicators of a specific component. Finally, we correlated the Z-maps of the components with the untresholded Z-maps of the fMRI in FRs.

3 Results

3.1 Description of the included studies

The systematic literature search resulted in 29 fMRI studies (one study included two separate datasets that were analyzed separately, i.e. altogether 30 fMRI datasets) and 10 VBM studies (one study included two separate datasets, i.e. altogether 11 VBM datasets). All the studies were originally published between 2003 and 2018. The descriptive statistics of the included studies are presented in Table 1 (fMRI studies) and

Table 2 (VBM studies). In the fMRI studies, there were altogether 754 FRs (sample size weighted mean age=31.9 years; 56.8% female) and 959 healthy controls (sample size weighted mean age=30.5 years; 50.5% female). In the VBM studies, there were altogether 885 FRs (mean age=31.2 years; 51.6% female) and 775 healthy controls (mean age=30.8 years; 49.6% female). IQ was reported only in 15 datasets (11 fMRI and 4

VBM). These 15 datasets indicated that IQ was lower in FRs than in controls (sample size weighted mean=104.2 vs. 108.9, Z=-3.8, p<.0001) (forest plot available in Supplementary Figure 1). There was,

9 however, significant heterogeneity between the included studies (I2=71.30%, Q(14)=53, p<.0001) but no indication of publication bias (Egger's test, p=.62).

Regarding cognitive tasks in the fMRI studies, there were 19 studies with executive functioning tasks that were further classified into two categories: 11 datasets with working memory tasks

(the N-back working memory task; the Sternberg working memory task; Spatial delayed-response task) and

8 datasets with inhibitory control tasks (the Continuous Performance Task, Stop-Signal Anticipation Task;

Dot Probe Expectancy Task; Hayling Sentence Completion Task; Pro- and Antisaccades Task).

Additionally, there were seven studies with social cognition tasks (including Theory of Mind Task; Irony comprehension task; Facial processing tasks; Self-referential task).

There were 6 fMRI studies with such cognitive tasks that could not be classified into the previous categories. The cognitive tasks assessed reward anticipation (1 dataset), early visual processing (2 datasets), visual memory (1 dataset), auditory comprehension (1 dataset), and cognitive skills learning (1 dataset). These datasets were included in the first fMRI meta-analysis (with the full set of cognitive tasks).

3.2 Meta-analysis of the fMRI studies

The results of the fMRI meta-analysis are presented in Table 3. In the first analysis with the full set of cognitive tasks, FRs had increased activity in the right inferior frontal gyrus (opercular part) when compared to healthy controls. Supplementary Figure 2 provides the individual study estimates and an overall estimate of the activation difference in the right inferior frontal gyrus between FRs and healthy controls. The results remained basically the same when excluding studies that possibly included a few second-degree relatives of schizophrenia patients (two studies) (see Supplementary Table 5). However, when we excluded studies that did not employ any correction for multiple comparisons (eight studies), we found no group differences in the

BOLD response. The field strength, mean age of the FR group or the gender distribution in the familial risk group did not relate to fMRI findings as analyzed with meta-regression. In the fMRI meta-analysis, we detected no significant between-study heterogeneity in the right inferior frontal gyrus (I2=0.1%).

Additionally, the jackknife sensitivity analysis confirmed that the findings in the right inferior frontal gyrus

10 were reproducible (27/30). Moreover, there was no indication of publication bias in the right inferior frontal gyrus (Egger's test p=0.34) (see funnel plots in Supplementary Figure 3).

During executive functioning tasks and working memory tasks, FRs had increased activity in the right inferior frontal gyrus when compared to healthy controls. No other group differences during these tasks were detected. Further, no findings were observed in social cognition and inhibition control studies in

BOLD response in FR vs. controls.

3.3 Meta-analysis of the VBM studies

The meta-analysis of the VBM studies showed that there were no differences in gray matter volume between

FRs and healthy controls. No findings were also found when excluding studies that possibly included a few second-degree relatives of schizophrenia patients (two studies). In addition, there were no group differences in grey matter density when excluding studies that did not employ any correction for multiple comparisons

(three studies).

3.4 Multimodal meta-analysis of the fMRI and VBM studies

In the multimodal analyses, we included both fMRI and VBM studies in the same meta-analysis, in order to assess whether some brain regions exhibited both functional and structural alterations. Multimodal analyses obtained no differences between FRs and healthy controls.

3.5 Comparison to the meta-analysis of previous fMRI and VBM studies in schizophrenia patients

We investigated whether brain regions that exhibited functional in FRs overlapped with brain regions that showed both functional and structural alterations in schizophrenia. The results are shown in Figure 2. In the full set of fMRI studies, FRs had increased activity in the right inferior frontal gyrus. This region was slightly overlapping with the brain regions that were affected in schizophrenia patients.

3.6 Comparison to the meta-analysis of previous behavioral fMRI studies in healthy individuals

11 Principal component analysis of the brain activity patterns of different behavioral domains resulted in a two- component solution (76% of the variance explained). The component structure was further promax rotated.

The loadings of all the 47 behavioral domains on the two components are shown in Figure 3a. The first component had factor loadings from the domains of executive functioning, inhibition, attention, working memory, spatial reasoning, and language processing. The second component included factor loadings from processing negative and positive affect, interoceptive processing, and sensory processing. Consequently, the first component was named as “cognitive component” and the second component as “affect/sensory component”.

Next, we investigated whether the untresholded FR vs. control map might correlate with the cognition- or affect/sensory-related brain activity maps in healthy individuals. The results are shown in

Figures 3b and 3c. Specifically, the brain activity maps of cognitive domains (in healthy individuals) correlated positively with the untresholded FR vs. controls meta-analysis map. In contrast, the brain activity maps of affect/sensory-related domains (in healthy individuals) did not correlate with the untresholded FR vs. controls meta-analysis map. Taken together, the brain region that exhibited functional alterations in FRs seemed to correlate with the brain activity maps that are activated during cognitive tasks in healthy individuals.

4 Discussion

To the best of our knowledge, this is the largest multimodal coordinate-based meta-analysis on first-degree relatives of schizophrenia patients (754 FRs in fMRI studies and 885 FRs in VBM studies). We found that

FRs exhibit very restricted and only slightly overlapping functional alterations with those seen in schizophrenia patients. We also found that the brain regions that exhibited altered functioning in FRs correlated positively with the brain regions that exhibit increased activity during cognitive tasks in healthy individuals. The VBM meta-analysis and multimodal analyses obtained no differences between FRs and healthy controls.

12 4.1 fMRI meta-analyses on different cognitive tasks

In the full set of cognitive tasks, FRs had higher activity in the right frontal gyrus when compared to healthy controls. This difference was also obtained during working memory tasks and executive functioning tasks.

Previously, the right frontal gyrus is found to response inhibition and attentional control (Aron et al., 2003;

Chikazoe et al., 2007; Hampshire et al., 2010). These abilities, in turn, are known to be impaired in schizophrenia patients (Enticott et al., 2008; Wang et al., 2005). Hence, the findings suggest that some impairments in the neurobiological basis of these abilities may familial risk of psychosis.

Previously, single studies have suggested that during executive functioning tasks, FRs might exhibit altered activity in regions that are not generally related to executive functioning, such as anterior cingulate gyrus (Filbey et al., 2008) or corpus callosum (Karch et al., 2009). However, this meta-analysis did not support these findings. Specifically, we obtained functional alterations in FRs during cognitive tasks only in the right frontal gyrus, and the untresholded group comparison map correlated positively with those brain regions that exhibit increased activity during cognitive tasks in healthy individuals.

We found that the right frontal gyrus exhibited higher activity in FRs (vs. controls) during cognitive tasks. We speculate that this finding might relate to the neural compensatory mechanism, where individuals with FR compensate for the difficulty of the task via hyperactivation. Similar conclusions have been made in previous studies (Cooper et al., 2014; Fusar-Poli et al., 2010; Pulkkinen et al., 2015). This conclusion is in line with a previous meta-analysis that found the activity of the right inferior frontal gyrus decreasing in FRs during rest (Niu et al., 2017).

The brain regions with increased activity in FRs during cognitive tasks appeared to be mostly located in the right hemisphere. This may be related to the lateralization hypothesis of schizophrenia (Crow et al., 1989), postulating that schizophrenia is linked to weaker lateralization of the brain functioning. For example, schizophrenia patients have an abnormal right hemisphere dominance during language processing tasks (Li et al., 2009). Additionally, schizophrenia patients have a higher prevalence of left-handedness, indicating a dominance of the right brain hemisphere in motor functioning (Dragovic et al., 2005). In line with this, we found that FRs had increased activity in the right hemisphere during cognitive tasks.

13 It has been suggested that the differences between FRs and healthy controls may be at least partially explained by differences in IQ (de Zwarte et al., 2018). In this meta-analysis, we found that IQ was lower in FR when compared with controls. Note, nonetheless, that the average IQ in FRs was 104, which is even slightly above the average IQ. Overall, we suggest that future studies should extend the neuroimaging research on intelligence and psychosis risk.

4.2 VBM meta-analysis

The VBM meta-analysis showed that there were no differences between FRs and healthy controls in gray matter density in the brain. This finding may be related to a variety of issues. Firstly, population-based studies have demonstrated that the gray matter volume steadily decreases from middle childhood or early adolescence onwards (Sowell et al., 2003). Along with this, the previous findings of greater gray matter volume in high-risk individuals are suggested to be explained by study sampling: in several samples, the participants with prodromal syndromes have been younger than healthy controls (Hirayasu et al., 2001). The results of our meta-analysis are in line with this since the mean age of the FRs, and healthy controls was approximately the same (31.9 years in FRs and 30.5 years in healthy controls), and no structural differences were obtained.

Secondly, it has been suggested that some of the alterations in FRs may be present only in those FRs who will develop psychosis later in life (Fusar-Poli et al., 2012). Further, the structural alterations are found to correlate with the duration of the illness and the use of medications (van Erp et al., 2018). In our meta-analysis, we excluded those studies that included FRs with psychotic symptomatology and obtained no structural alterations in FRs. Hence, it is possible that only those FRs who will convert later into psychosis have structural alterations in the brain. However, no firm conclusions can be made about converters vs. non-converters because the data did not provide possibilities to analyze structural differences between converters vs. non-converters.

Previous ENIGMA studies have shown that schizophrenia patients have smaller volumes in a variety of subcortical regions (e.g. in the hippocampus, amygdala, thalamus, and nucleus accumbens, and larger lateral ventricle) and cortical regions (e.g. thinner cortex and smaller surface area especially in the

14 frontal and temporal regions) (van Erp et al., 2016, 2018). Regarding FRs, however, it has been suggested that genetically-based abnormalities in the brain structure among FRs are “neither severe nor always specific” and more restricted by location in FRs than in schizophrenia patients (Lieberman et al., 2001).

Along with this, our findings suggest that FRs exhibit no similar alterations in gray matter volume compared to schizophrenia patients. The observed modest activation differences in FRs vs. controls are in line with a recent ENIGMA study (1228 FRs and 2246 controls) that found relatively weak effect sizes for the structural brain differences between FR and controls (de Zwarte et al., 2019). Overall, it appears that the possible FR-related neural alterations are subtle and large sample sizes are required to observe the effect of

FR on brain structures.

Overall, the VBM meta-analyses among FRs have resulted in inconclusive findings. For example, in Cooper et al. (2014) meta-analysis, FRs were found to have larger grey matter volume in the left medial frontal gyrus and smaller grey matter volume in left thalamus/putamen, right superior frontal gyrus, and left insula, when compared to controls. In our meta-analysis, however, we obtained no structural differences between FRs and healthy controls. The divergent findings of the VBM meta-analyses in FRs may be related to differences in the sample size. Although we had a larger sample size compared to Cooper et al. (2014), however, it is possible that our study was still underpowered, since previous large ENIGMA study in FRs found subtle structural alterations in FRs vs. controls. Additionally, the definition of "familial risk for schizophrenia" has been varying: for example, contrary to our meta-analysis, a first-degree relationship with schizophrenia patients was not required in Cooper's et al. (2014) meta-analysis. Finally, in recent years, there has been an increasing concern about false positives in neuroimaging studies.

4.3 Multimodal meta-analysis

In the multimodal analyses, we included both fMRI and VBM studies in the same meta-analysis, in order to see whether some brain regions exhibited both functional and structural alterations. No differences were obtained between FRs and healthy controls. This is in line with the previous meta-analysis that also obtained no differences between FRs and healthy controls in the multimodal analyses (Niu et al., 2017). Among patients with first episode psychosis, it has been found that the use of antipsychotics correlates with

15 alterations in the regions that exhibit conjoint structural and functional alterations (Radua et al., 2012a).

Hence, it may be that multimodal alterations may be obtained only after the onset of psychosis.

4.4 Methodological considerations

The number of fMRI studies investigating social cognition was comparatively low (7 studies), whereas the optimal number of studies for meta-analysis is likely higher (Eickhoff et al., 2016). In the case of such a low number of studies, also differences in fMRI tasks may be a source of heterogeneity. However, this same challenge has also been present in other coordinate-based meta-analyses. For example, in the previous VBM meta-analysis investigating the functional changes during cognitive tasks in individuals at genetic risk for schizophrenia, there were only 6 VBM studies (Cooper et al., 2014). Thus, it appears that more research reporting voxel-wise results are needed in order to be able to conduct a reliable coordinate-based meta- analysis on different behavioral tasks in the future. Overall, future meta-analyses should investigate social cognition-related alterations in FRs when a larger number of studies are available.

Secondly, it could be argued that the reason for investigating alterations in FRs is that they are known to have an elevated risk for psychotic symptoms and for the use of antipsychotic medications. In this meta-analysis, however, we excluded studies where FRs had psychotic symptoms or had used antipsychotic medications. This is because there is evidence that the onset of psychosis is characterized by decreases of gray matter in a variety of brain regions (e.g. temporal and frontal regions) (Fusar-Poli et al., 2011).

Additionally, exposure to antipsychotic drugs is shown to be related to structural alterations in the brain (e.g. insula and anterior cingulate) (Radua et al., 2012a; van Erp et al., 2018). Moreover, individuals with psychotic symptoms or antipsychotic medications have also been excluded in several previous meta- analyses (e.g. Cooper et al., 2014; Scognamiglio et al., 2014; Zhang et al., 2016). Consequently, antipsychotic medications or psychotic symptoms could potentially have confounded the association of familial risk for schizophrenia with structural and functional alterations in the brain.

Thirdly, this meta-analysis included studies that had investigated participants with at least one first-degree relative with schizophrenia. This methodological choice has also been used in the previous meta-analyses (Cooper et al., 2014; Scognamiglio et al., 2014). We did not conduct separate analyses among

16 different types of first-degree relatives (i.e. offspring, parents, siblings). It has been shown that there may not exist significant structural differences in the brain between different types of first-degree relatives (de

Zwarte et al., 2018). Generally, it has been demonstrated that cognitive deficits are more severe in relatives with higher genetic risk for schizophrenia (Byrne et al., 2003; Faraone et al., 2000). Hence, the neural alterations in FRs may be even more evident in FRs with two first-degree relatives with schizophrenia.

Fourthly, after conducting the article search for this meta-analysis, two more recent studies have been published. The studies suggested that FRs may have a smaller total volume of the cortical and cerebellar gray matter and smaller volume in the thalamus, putamen, amygdala, and nucleus accumbens (de

Zwarte et al., 2018, 2019). Hence, the continuous accumulation of new research is necessary to take into consideration. Additionally, it is necessary to consider is that we utilized different software for the comparison of our findings to previous schizophrenia studies and behavioral domains. Thus, these analyses should be considered as supplementary analyses.

This meta-analysis had a variety of strengths. Firstly, we had a substantially higher number of studies and a higher total number of FRs (41 datasets, 1638 FRs) than in the largest previous meta-analysis

(25 datasets, 1065 FRs) (Boos et al., 2007) that was published before the conduction of our analyses. A recently published ENIGMA study (de Zwarte et al., 2019), however, included more participants than our meta-analysis (1228 FR and 2246 controls). Secondly, we also performed a multimodal meta-analysis to investigate whether the functional and structural alterations occur in overlapping brain regions. Thirdly, to the best of our knowledge, this meta-analysis was the first to investigate functional alterations in FRs separately in various cognitive tasks. Finally, we investigated whether the brain regions with structural or functional alterations in FRs are overlapping with the brain regions (i) that are affected among schizophrenia patients and (ii) that exhibit increased activity during cognitive tasks in healthy individuals.

4.6 Conclusions

In summary, the fMRI meta-analysis showed that during cognitive tasks, FRs had increased activity in the right inferior frontal gyrus when compared to healthy controls. Overall, the functional alterations in FRs were very restricted and only slightly overlapping with the affected brain regions in schizophrenia patients.

17 The functional alterations in FRs correlated positively with the brain regions that exhibited increased activity during cognitive tasks in healthy individuals. The VBM meta-analysis or multimodal analyses obtained no differences between FRs and healthy controls. It is necessary to consider that due to the comparatively low number of studies with some types of cognitive tasks (e.g. social cognition tasks), no firm conclusions about task-specific alterations in FRs can be made. Consequently, more research is needed about functional alterations on a broader range of different cognitive tasks. In conclusion, our findings suggest that there may exist minor functional alterations in the brain in FRs (vs. controls) in various cognitive domains that have a role in the pathogenesis of schizophrenia. Instead, we did not find any structural alterations in FRs.

Acknowledgments

This study was supported financially by the Yrjö Jahnsson Foundation (J.L.), Jalmari and Rauha Ahokas

Foundation (J.L.), and the Finnish Medical Foundation (J.L.).

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28 Table 1. Description of the fMRI studies included in the meta-analysis.

FRs Healthy controls Publication Mean age Mean age Software Smoothing First author year N Female (%) (years) N Female (%) (years) package Tesla kernel (mm) Cognitive task Working memory Callicott, J.H.a 2004a 23 74 34 18 39 30 SPM 1.5 10 N-back working memory task Callicott, J.H.a 2004b 25 56 37 15 60 28 SPM 1.5 10 N-back working memory task Choi, J.S. 2011 17 47 21 16 44 21 SPM 1.5 8 Spatial delayed-response task (spatial working memory) De Leeuw, M. 2013 23 39 30 24 50 28 SPM 3 8 Sternberg Working Memory Task Jiang, S. 2015 20 45 51 20 50 52 SPM 3 8 N-back working memory task Karch, S. 2009 11 64 34 11 64 34 Brainvoyager 1.5 8 N-back working memory task Li, X. 2016 43 71 25 32 59 25 FSL 3 8 Visual and verbal 1-back working memory task Loeb, F.F. 2018 30 43 19 39 46 20 AFNI 3 8 1- and 2-back working memory tasks Meda, S.A. 2008 23 61 51 43 53 43 SPM 3 12 Sternberg Working Memory Task Stäblein, M. 2018 22 64 43 25 52 35 BrainVoyager 3 NA Visual working memory task (a masked change detection task) Zandbelt, B.B. 2011 24 46 30 24 38 32 SPM5 3.0 6 Sternberg Working Memory Task; Stop- Signal Anticipation Task (inhibitory control)

Inhibitory control Becker, T.M. 2008 17 65 33 17 41 33 AFNI 3 8 Stroop task 2008 30 53 21 92 58 20 NA 1.5 8 Continuous performance task (the AX- Delawalla, Z. CPT) Lopez-Garcia, P. 2016 16 44 57 20 60 33 SPM 3 8 Dot Probe Expectancy Task (context processing) Raemaekers, M. 2006 16 50 34 16 50 33 IDL 1.5 8 Pro- and antisaccades task (eye movement control) Sepede, G. 2009 11 55 34 11 55 32 BrainVoyager 1.5 NA Continuous Performance Test (sustained attention) Whalley, H.C. 2004 69 57 26 21 38 27 SPM99 1.5 6 Hayling Sentence Completion Task (response intiation and suppression)

29 Zandbelt, B.B. 2011 24 46 30 24 38 32 SPM5 3.0 6 Sternberg Working Memory Task; Stop- Signal Anticipation Task (inhibitory control)

Social cognition Dodell-Feder, D. 2014 19 74 27 18 78 26 SPM 3 6 Theory of mind tasks (Person-Description task; False-Belief Task) Herold, R. 2018 12 50 43 12 58 37 FSL 3 5 Irony comprehension task Li, X. 2012 12 66 31 12 50 29 SPM 3 6 Facial emotional valence discrimination Park, H.Y. 2016 20 65 24 17 53 23 SPM 3 8 Facial emotion processing task Pirnia, T. 2015 14 64 40 30 20 29 FSL 3 6 Facial memory task (face-name encoding and retrieval) Spilka, M.J. 2017 27 63 41 27 52 41 FSL 3 7 Facial emotion and age recognition task Van Buuren, M. 2012 25 56 28 25 56 28 SPM5 3.0 8 Self-referential task (social cognition)

Other tasks Grimm, O. 2014 54 57 34 80 51 34 SPM 3 9 Monetary reward anticipation paradigm Lee, J. 2010 21 52 36 19 26 43 FSL 3 5 Visual backward masking task Rajarethinam, R. 2011 15 53 15 17 47 15 SPM 4 8 Auditory comprehension task Rasetti, R. 2014 65 58 36 181 52 35 SPM 3 8 Declarative memory task (visual encoding) Stolz, E. 2012 16 63 23 28 68 27 SPM 3 8 Visual episodic memory encoding and retrieval task Wagshal, D. 2013 10 50 13 25 40 13 FSL 3 5 Weather Prediction Task (cognitive skill learning task) a Callicott et al. (2004) study included two datasets that were treated separately in the meta-analysis. NA = Information not available. AFNI = Analysis of Functional NeuroImages. SPM = Statistical parametric mapping. FSL = The FMRIB Software Library. IDL = The Interactive Data Language.

30 Table 2. Description of the VBM studies included in the meta-analysis.

Publicatio FRs Healthy controls Software Smoothing First author n year N Female (%) Age (years) N Female (%) Age (years) package Tesla kernel (mm) Boos, H.B.M. 2011 186 54 28 122 50 28 Other 1.5 8 Guo, W. 2015 46 37 23 46 50 23 SPM 3.0 8 Guo, W. 2014 25 32 23 43 42 24 SPM 3.0 8 Honea, R.A. 2008 213 58 36 212 51 33 SPM2 1.5 6 Job, D.E. 2003 146 49 21 36 53 21 SPM99 1.0 12 Lei, W.a 2015a 25 48 44 40 55 43 SPM8 3.0 6 Lei, W.a 2015b 42 55 43 40 55 43 SPM8 3.0 6 McIntosh, A.M. 2004 24 54 39 49 53 35 SPM 1.5 8 Tian, L. 2011 55 51 50 29 52 52 SPM5 / VBM5 3.0 6 Van der Velde, J. 2015 89 54 32 69 45 34 SPM 3.0 8 Wagshal, D. 2015 14 43 12 46 46 13 FSL 3.0 3 a The study included two datasets that were treated separately in the meta-analysis. SPM = Statistical parametric mapping. FSL = The FMRIB Software Library.

31 Table 3. Brain regions with altered activation in FRs (fMRI studies) and altered gray matter volume in FRs (VBM studies) in the multimodal meta-analysis, when compared to healthy controls.

Coordinates (MNI) Test statistic of SDM p Voxels Description fMRI studies Full set of cognitive tasks FRs > Controls 46, 12, 32 2.158 0.000001967 616 Right inferior frontal gyrus, opercular part, BA 44 Executive functioning FRs > Controls 50,16,28 2. 485 0.000003099 553 Right inferior frontal gyrus, opercular part, BA 48 Working memory FRs > Controls 50, 12, 26 2.443 0.000003219 913 Right inferior frontal gyrus, opercular part, BA 44

32 fMRI studies VBM studies Articles identified through database search Articles identified through database search N=447 N=427

Articles defined as eligible Articles defined as eligible after screening the title and abstract after screening the title and abstract N=174 N=61

Articles identified through reference list search Articles identified through reference list search N=0 N=0

Articles defined as eligible after screening for Articles defined as eligible after screening for inclusiong/exclusion criteria inclusiong/exclusion criteria N=29 N=11

fMRI studies included VBM studies included in the meta-analysis in the meta-analysis N=29 (30 datasets) N=10 (11 datasets)

Figure 1. The selection process of the fMRI and VBM studies that were included in the meta- analysis.

33

Figure 2. (a) Brain regions with increased (red) or decreased (blue) activity (fMRI) or volume

(VBM) (in blue color) in FRs during different types of cognitive tasks, when compared to healthy controls. (b) Brain regions with overlap between meta-analyses in FRs and schizophrenia patients

(Sch).

34

Figure 3. (a) The results of the principal component analysis: the loadings of the brain activity patterns of different behavioral domains to the cognition- and affect/sensory-related components in healthy individuals. (b) The correlations of the brain activation patterns during cognition- and affect/sensory-related processing (in healthy individuals) with the brain regions that showed altered fMRI activity in FRs. (c) The correlations of the brain activation patterns during cognition and affect/sensory-related processing (in healthy individuals) with the brain regions that showed structural alterations in FRs.

35

Supplementary Figure 1. Forest plot of the standardized mean differences in the level of IQ between FRs and healthy controls (in the 15 datasets of this meta-analysis that reported IQ). Note:

RE=random effect, SMD=standardized mean difference.

36

Supplementary Figure 2. Forest plot depicting the individual study estimates and the overall estimate of the activation difference in the right inferior frontal gyrus (MNI coordinate: 46,12,32) between FRs and healthy controls. Note: RE=random effect, SMD=standardized mean difference.

37

Supplementary Figure 3. Funnel plot for the differences in the right inferior frontal gyrus (MNI coordinates: 46,12, 32). In Egger's test, p-value=0.34.

38 Supplementary Table 1. The fulfilled form of the MOOSE Checklist for Meta-analyses of

Observational Studies.

Reported on Item No Recommendation Page No Reporting of background should include 1 Problem definition Page 4 2 Hypothesis statement Page 4 3 Description of study outcome(s) Page 4 4 Type of exposure or intervention used Page 4 5 Type of study designs used Page 4 6 Study population Page 4

Reporting of search strategy should include

7 Qualifications of searchers (eg, librarians and investigators) Title page 8 Search strategy, including time period included in the synthesis and key words Pages 4-5 9 Effort to include all available studies, including contact with authors Pages 4-6 10 Databases and registries searched Page 4 Search software used, name and version, including special features used (eg, 11 Pages 4-5 explosion) 12 Use of hand searching (eg, reference lists of obtained articles) Page 5 Figure 1, 13 List of citations located and those excluded, including justification Supplementary Tables 1 and 2 14 Method of addressing articles published in languages other than English Page 5 15 Method of handling abstracts and unpublished studies Pages 4-5 16 Description of any contact with authors Page 6 Reporting of methods should include Description of relevance or appropriateness of studies assembled for assessing the 17 Pages 9-10 hypothesis to be tested Rationale for the selection and coding of data (eg, sound clinical principles or 18 Pages 5-6 convenience) Documentation of how data were classified and coded (eg, multiple raters, blinding 19 Page 6 and interrater reliability) Assessment of confounding (eg, comparability of cases and controls in studies where 20 Pages 10-11 appropriate) Assessment of study quality, including blinding of quality assessors, stratification or 21 Pages 9-11 regression on possible predictors of study results 22 Assessment of heterogeneity Page 7 Description of statistical methods (eg, complete description of fixed or random effects models, justification of whether the chosen models account for predictors of 23 Pages 6-9 study results, dose-response models, or cumulative meta-analysis) in sufficient detail to be replicated 24 Provision of appropriate tables and graphics Pages 6-9

Reporting of results should include

39 Supplementary 25 Graphic summarizing individual study estimates and overall estimate figure 2 26 Table giving descriptive information for each study included Tables 1 and 2 27 Results of sensitivity testing (eg, subgroup analysis) Pages 10-11 28 Indication of statistical uncertainty of findings Pages 10-11

Reported Item No Recommendation on Page No Reporting of discussion should include

29 Quantitative assessment of bias (eg, publication bias) Pages 13-14 30 Justification for exclusion (eg, exclusion of non-English language citations) Page 16 31 Assessment of quality of included studies Pages 15-17 Reporting of conclusions should include 32 Consideration of alternative explanations for observed results Page 17 Generalization of the conclusions (ie, appropriate for the data presented and within the 33 Page 17-18 domain of the literature review) 34 Guidelines for future research Page 17-18 (Reported in 35 Disclosure of funding source another document) Source: Stroup, D.F., Berlin, J.A., Morton, S.C., et al., 2000. For the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) Group. Meta-analysis of Observational Studies in Epidemiology. A Proposal for Reporting. JAMA 283, 2008-2012.

40 Supplementary Table 2. The fMRI studies that were identified in literature search on the basis of title and abstract and that were excluded in the later phase.

First author Published Title Reason for exclusion Altamura, M. 2012 Abnormal functional motor lateralization in healthy siblings of patients with schizophrenia ROI-based analyses Antonucci L. A. 2016 Association of familial risk for schizophrenia with thalamic and medial prefrontal functional ICA was used connectivity during attentional control Barbour, T. 2012 fMRI responses to emotional faces in children and adolescents at genetic risk for psychiatric Coordinates or Z/T-statistics not illness share some of the features of depression available Barbour, T. 2010 Reduced intra-amygdala activity to positively valenced faces in adolescent schizophrenia ROI-based analyses offspring Bedwell, J. S. 2006 Schizophrenia and red light: fMRI evidence for a novel biobehavioral marker ROI-based analyses Bonner-Jackson, A. 2007 Levels-of-processing effects in first-degree relatives of individuals with schizophrenia High-risk individuals and healthy controls not compared Brahmbhatt, S. B. 2006 Neural correlates of verbal and nonverbal working memory deficits in individuals High-risk individuals and healthy with schizophrenia and their high-risk siblings controls not compared Braun, U. 2016 Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism Only brain network analyses modulated by NMDA receptor function Brent, B. 2014 Neural responses during social reflection in relatives of schizophrenia patients: Relationship ROI-based analyses to subclinical delusions Chahine, G. 2017 Disruptions in the left frontoparietal network underlie resting state endophenotypic markers Only brain network analyses in schizophrenia Chang, X. 2014 Altered default mode and fronto-parietal network subsystems in patients ICA used with schizophrenia and their unaffected siblings Collin, G. 2011 Impaired cerebellar functional connectivity in schizophrenia patients and their healthy siblings Only functional connectivity analyses

Dauvermann, M. R. 2013 The application of nonlinear Dynamic Causal Modelling for fMRI in subjects at high genetic ROI-based analyses risk of schizophrenia de Achaval, D. 2013 Activation of brain areas concerned with social cognition during moral decisions is abnormal Coordinates or Z/T-statistics not inschizophrenia patients and unaffected siblings available de Achával, D. 2012 Decreased activity in right-hemisphere structures involved in social cognition Coordinates or Z/T-statistics not in siblings discordant for schizophrenia available

41 Di Giorgio, A. 2013 Evidence that hippocampal-parahippocampal dysfunction is related to genetic Coordinates or Z/T-statistics not risk for schizophrenia available Dodell-Feder, D. 2014 The relationship between default mode network connectivity and social functioning in ROI-based analyses individuals at familial high-risk for schizophrenia Egan, M. F. 2001 Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia High-risk individuals and healthy controls not compared Fahim, C. 2004 Genes and memory: the neuroanatomical correlates of emotional memory in monozygotic High-risk group size < 10 twin discordant for schizophrenia Falkenberg, I. 2015 Failure to deactivate medial prefrontal cortex in people at high risk for psychosis Not peer-reviewed original article Ganella, E. P. 2018 Risk and resilience brain networks in treatment-resistant schizophrenia Only functional connectivity analysis Goghari, V. M. 2011 Executive functioning-related brain abnormalities associated with the genetic liability Not peer-reviewed original article for schizophrenia: an activation likelihood estimation meta-analysis Goghari, V. M. 2017 Task-Related Functional Connectivity Analysis of Emotion Discrimination in a Family Study Only functional connectivity analysis of Schizophrenia Goldberg, T. E. 2006 The G72/G30 gene complex and cognitive abnormalities in schizophrenia High-risk individuals and healthy controls not compared Guo, S. 2018 The instability of functional connectivity in patients with schizophrenia and their siblings: A Only functional connectivity analysis dynamic connectivity study Guo, W. 2014 Decreased default-mode network homogeneity in unaffected siblings of schizophrenia patients ROI-based analyses Guo, W. 2014 Decreasedat rest. regional activity of default-mode network in unaffected siblings of schizophrenia ICA was used patients at rest Guo, W. 2014 Decreased resting-state interhemispheric functional connectivity in unaffected siblings of Only functional connectivity analysis schizophrenia patients Guo, W. 2015 Dissociation of functional and anatomical brain abnormalities in unaffected siblings of Resting-state schizophrenia patients Guo, W. 2017 Family-based case-control study of homotopic connectivity in first-episode, drug- Only functional connectivity analysis naive schizophrenia at rest Guo, W. 2015 Resting-state cerebellar-cerebral networks are differently affected in first-episode, drug-naïve Only functional connectivity analysis schizophrenia patients and unaffected siblings Guo, W. 2017 Hyperactivity of the default-mode network in first-episode, drug-naive schizophrenia at rest Resting-state revealed by family-based case-control and traditional case-control designs Guo, W. 2017 Using short-range and long-range functional connectivity to identify schizophrenia with a Only functional connectivity analysis family-based case-control design

42 Guo, W. 2015 Increased Cerebellar Functional Connectivity With the Default-Mode Network in Only functional connectivity analysis Unaffected Siblings ofSchizophrenia Patients at Rest Habel, U. 2004 Genetic load on amygdala hypofunction during sadness in nonaffected brothers ROI-based analyses of schizophrenia patients Hager, B. 2017 Neural complexity as a potential translational biomarker for psychosis Brain activation levels not investigated Hanssen, E. 2015 Neural correlates of reward processing in healthy siblings of patients with schizophrenia Coordinates or Z/T-statistics not available Harms, M. P. 2013 Structure-function relationship of working memory activity with hippocampal and prefrontal High-risk individuals and healthy cortex volumes controls not compared Hart, S. J. 2013 Altered fronto-limbic activity in children and adolescents with familial high risk Antipsychotic medications among high- for schizophrenia risk individuals Hart, S. J. 2015 Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Antipsychotic medications among high- Familial High Risk for Schizophrenia risk individuals Hass, J. 2015 Complexin2 modulates working memory-related neural activity in patients with schizophrenia High-risk individuals and healthy controls not compared Jang, J. H. 2011 Reduced prefrontal functional connectivity in the default mode network is related to greater Only functional connectivity analyses psychopathology in subjects with high genetic loading for schizophrenia Jukuri, T. 2015 Central executive network in young people with familial risk for psychosis--the Oulu Brain Resting-state and Mind Study Jukuri, T. 2015 Cerebellar activity in young people with familial risk for psychosis--The Oulu Brain and Mind Resting-state Study Jukuri, T. 2013 Default mode network in young people with familial risk for psychosis--the Oulu Brain and Resting-state Mind study Karbasforoushan, H. 2012 Resting-state networks in schizophrenia Not peer-reviewed original article Karlsgodt, K. H. 2007 The relationship between performance and fMRI signal during working memory in patients ROI-based analyses with schizophrenia, unaffected co-twins, and control subjects Keshavan, M. S. 2002 A preliminary functional magnetic resonance imaging study in offspring of schizophrenic High-risk individuals and healthy parents controls not compared Kim, D. 2015 Shared and Distinct Neurocognitive Endophenotypes of Schizophrenia and Psychotic Bipolar Not fMRI study Disorder Landin-Romero, R. 2015 Failure of deactivation in the default mode network: a trait marker for schizophrenia? High-risk individuals and healthy controls not compared Langbein, K. 2011 Functional MRI in twins discordant for schizophrenia during a working memory task: Not peer-reviewed original article preliminary results from the Eutwinss Study

43 Langbein, K. 2010 Functional MRI of working memory related activation in monozygotic wtins discordant for Not peer-reviewed original article schizophrenia: preliminary results from the Eutwinss Study Lawrie, S. M. 2008 Brain structure and function changes during the development of schizophrenia: The evidence Not peer-reviewed original article from studies of subjects at increased genetic risk Lawrie, S. M. 2003 Structural and functional abnormalities of the amygdala in schizophrenia ROI-based analyses Lerner, Y. 2017 Abnormal neural hierarchy in processing of verbal information in patients with schizophrenia Not brain activation levels investigated Li, X. 2007 An fMRI study of language processing in people at high genetic risk for schizophrenia High-risk individuals and healthy controls not compared Li, X. 2010 Disturbed Functional Connectivity of Cortical Activation during Semantic Discrimination in Not brain activation levels investigated Patients with Schizophrenia and Subjects at Genetic High-risk Li, X. 2007 fMRI study of language activation in schizophrenia, schizoaffective disorder and in ROI-based analyses individuals genetically at high risk Li, X. 2009 Language pathway abnormalities in schizophrenia: a review of fMRI and other imaging Not peer-reviewed original article studies Liao, H. 2012 A resting-state functional magnetic resonance imaging study on the first-degree relatives of Resting-state study persons with schizophrenia Lindberg, P. G. 2016 Altered cortical processing of motor inhibition in schizophrenia ROI-based analyses Liu, J. 2007 A novel approach to analyzing fMRI and SNP data via parallel independent component High-risk individuals and healthy analysis controls not compared Liu, M. 2012 Potential risk for healthy siblings to develop schizophrenia: evidence from pattern High-risk individuals and healthy classification with whole-brain connectivity controls not compared Liu, M. 2011 Schizophrenic Patients and Their Unaffected Siblings Show Similar Abnormalities in Resting- ROI-based analyses State Functional Connectivity Lo, C. Y. 2015 Randomization and resilience of brain functional networks as systems-level endophenotypes Coordinates or Z/T-statistics not of schizophrenia available Lui, S. 2015 Resting-state brain function in schizophrenia and psychotic bipolar probands and their first- Not brain activation levels investigated degree relatives MacDonald, A. W. 2003 Context processing deficits associated with hypofrontality in the No access to full-text version healthy relatives of schizophrenia patients: An event-related fMRI study MacDonald, A. W. 2006 Functional magnetic resonance imaging study of cognitive control in the healthy relatives of High-risk individuals and healthy schizophrenia patients controls not compared Marjoram, D. 2006 A visual joke fMRI investigation into Theory of Mind and enhanced risk of schizophrenia High-risk individuals and healthy controls not compared

44 Meda, S. 2012 Differences in Resting-State Functional Magnetic Resonance Imaging Functional Network Not brain activation levels investigated Connectivity Between Schizophrenia and Psychotic Bipolar Probands and Their Unaffected First-Degree Relatives Meda, S. 2015 Frequency-Specific Neural Signatures of Spontaneous Low-Frequency Resting State Not brain activation levels investigated Fluctuations in Psychosis: Evidence From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium Mohnke, S. 2016 Theory of mind network activity is altered in subjects with familial liability for schizophrenia ROI-based analyses Niu, Y. 2017 Altered gray matter and brain activity in patients with schizophrenia and their Not peer-reviewed original article unaffected relatives: a multimodal meta-analysis of voxel-based structural MRI and resting- state fMRI studies Nook, E. C. 2018 Weak dorsolateral prefrontal response to social criticism predicts worsened mood and ROI-based analyses symptoms following social conflict in people at familial risk for schizophrenia Oertel-Knöchel, V. 2014 Association between symptoms of psychosis and reduced functional connectivity of auditory Only functional connectivity analysis cortex Oertel-Knöchel, V. 2013 Reduced functional connectivity and asymmetry of the planum temporale in patients Only functional connectivity analysis with schizophrenia and first-degree relatives Pearlson, G. D. 2017 Applications of Resting State Functional MR Imaging to Neuropsychiatric Diseases Not peer-reviewed original article Pearlson, G. D. 2011 Exploring connectivity differences among low-frequency resting-state fMRI networks Not peer-reviewed original article between schizophrenia and psychotic bipolar subject and their unaffected first-degree relatives Pearlson, G. D. 2009 Resting State and Default Mode fMRI Data in Schizophrenia, Bipolar Disorder, and Relatives No access to full-text version Pearlson, G. D. 2005 Working memory deficits and aberrant prefrontal cortex activation in first- No access to full-text version degree relatives ofschizophrenia patients: An fMRI study Peeters, S. 2015 Semi-metric analysis of the functional brain network: Relationship with familial risk for Only functional network analysis psychotic disorder Pettersson-Yeo, W. 2011 Dysconnectivity in schizophrenia: Where are we now? Not peer-reviewed original article Picchioni, M. M. 2010 Genetic and non-genetic influences on brain function in schizophrenia: an fMRI study in Not peer-reviewed original article twins Picchioni, M. M. 2001 Verbal fluency in twins with schizophrenia: An fMRI study High-risk individuals and healthy controls not compared Poppe, A. B. 2015 Task-based functional connectivity as an indicator of genetic liability to schizophrenia Only functional connectivity analysis Pruitt, P. 2009 Disordered functional maturation of the amygdala during adolescence: fMRI studies of Not peer-reviewed original article affective judgment in schizophrenia offspring Pulkkinen, J. 2015 Functional mapping of dynamic happy and fearful facial expressions in young adults Not necessarily 1st degree relatives with familial risk for psychosis - Oulu Brain and Mind Study

45 Rasetti, R. 2009 Evidence that altered amygdala activity in schizophrenia is related to clinical state and ROI-based analyses not genetic risk Repovs, G. 2011 Brain Network Connectivity in Individuals with Schizophrenia and Their Siblings Only network connectivity analysis Sambataro, F. 2010 Abnormal Cognitive Control Processing in Unaffected Siblings of Patients Not peer-reviewed original article with Schizophrenia: An fMRI Intermediate Phenotype for Schizophrenia? Schmidt, A. 2015 Approaching a network connectivity-driven classification of the psychosis continuum: a Not peer-reviewed original article selective review and suggestions for future research Schneider, M. 2017 Altered DLPFC-Hippocampus Connectivity During Working Memory: Independent Coordinates or Z/T-statistics not Replication and Disorder Specificity of a Putative Genetic Risk Phenotype for Schizophrenia available Scognamiglio, C. 2014 A meta-analysis of fMRI studies in healthy relatives of patients with schizophrenia Not peer-reviewed original article Seidman, L. J. 2006 Altered brain activation in dorsolateral prefrontal cortex in adolescents and young adults ROI-based analyses at genetic risk for schizophrenia: an fMRI study of working memory Seidman, L. J. 2007 Auditory verbal working memory load and thalamic activation in nonpsychotic relatives of ROI-based analyses persons with schizophrenia: an fMRI replication Seidman, L. J. 2014 Medial temporal lobe default mode functioning and hippocampal structure as vulnerability ROI-based analyses indicators for schizophrenia: a MRI study of non-psychotic adolescent first-degree relatives Sommer, I. E. 2004 Language activation in monozygotic twins discordant for schizophrenia ROI-based analyses Spaniel, F. 2007 Language lateralization in monozygotic twins discordant and concordant for schizophrenia: ROI-based analyses A functional MRI pilot study. Spilka, M. J. 2015 Functional activation abnormalities during facial emotion perception in schizophrenia patients Larger sample of the same population and nonpsychotic relatives provided elsewhere Su, J. 2016 Heredity characteristics of schizophrenia shown by dynamic functional connectivity analysis Resting-state study of resting-state functional MRI scans of unaffected siblings Tang, Y. 2015 Neural activity changes in unaffected children of patients with schizophrenia: A resting- Not activation investigated state fMRI study Thermenos, H. W. 2013 Altered language network activity in young people at familial high-risk for schizophrenia Relatives expressed psychotic Thermenos, H. W. 2007 Elaborative verbal encoding and altered anterior parahippocampal activation in adolescents ROIsymptomatology-based analyses and young adults at genetic risk for schizophrenia using FMRI Thermenos, H. W. 2004 Functional magnetic resonance imaging during auditory verbal working memory in ROI-based analyses nonpsychotic relatives of persons with schizophrenia: a pilot study Tian, L. 2011 Convergent evidence from multimodal imaging reveals amygdala abnormalities in Not activation investigated schizophrenic patients and their first-degree relatives

46 van Buuren, M. 2011 Exaggerated Brain Activation During Emotion Processing in Unaffected Siblings of Patients Larger sample of the same population with Schizophrenia provided elsewhere van der Meer, L. 2014 Neural correlates of emotion regulation in patients with schizophrenia and non- Larger sample of the same population affected siblings provided elsewhere van Leeuwen, J. M. C. 2018 At-risk individuals display altered brain activity following stress Larger sample of the same population provided elsewhere Wang, J. 2015 Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their Only functional connectivity analysis unaffected siblings Wang, Z. 2015 Large-Scale Fusion of Gray Matter and Resting-State Functional MRI Reveals Common and Resting-state study Distinct Biological Markers across the Psychosis Spectrum in the B-SNIP Cohort Watsky, R. E. 2016 Attenuated Resting-State Functional Connectivity in Patients with Childhood- and Adult- Resting-state study OnsetSchizophrenia, Not in Their Siblings Watsky, R. E. 2018 Attenuated resting-state functional connectivity in patients with childhood- and adult- Resting-state study onset schizophrenia Whalley, H. C. 2012 Effects of a mis-sense DISC1 variant on brain activation in two cohorts at high risk of bipolar High-risk individuals and healthy disorder orschizophrenia controls not compared Whalley, H. C. 2012 Impact of a microRNA MIR137 Susceptibility Variant on Brain Function in People at First-degree relatives and healthy High Genetic Risk of Schizophrenia or Bipolar Disorder controls not compared Whalley, H. C. 2009 fMRI changes over time and reproducibility in unmedicated subjects at high genetic Coordinates or Z/T-statistics not risk of schizophrenia available Whalley, H. C. 2005 Functional disconnectivity in subjects at high genetic risk of schizophrenia Not necessarily 1st degree relatives Whalley, H. C. 2006 Functional imaging as a predictor of schizophrenia Whitfield-Gabrieli, S. 2009 Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first- High-risk individuals and healthy degree relatives of persons with schizophrenia controls not compared Whyte, M. C. 2006 Event-related fMRI of word classification and successful word recognition in subjects at Larger sample of the same population genetically enhanced risk of schizophrenia provided elsewhere Villarreal, M. F. 2014 Pattern of brain activation during social cognitive tasks is related to social competence High-risk individuals and healthy in siblingsdiscordant for schizophrenia controls not compared Windemuth, A. 2008 Physiogenomic analysis of localized FMRI brain activity in schizophrenia ROI-based analyses Vink, M. 2016 DRD2 Schizophrenia-Risk Allele Is Associated With Impaired Striatal Functioning in ROI-based analyses Unaffected Siblings of Schizophrenia Patients Woodward, N. D. 2007 An FMRI investigation of procedural learning in unaffected siblings of individuals High-risk group size < 10 with schizophrenia

47 Woodward, N. D. 2009 Abnormal prefrontal cortical activity and connectivity during response selection in first High-risk group size < 10 episode psychosis, chronic schizophrenia, and unaffected siblings of individuals with schizophrenia Woodward, N. D. 2007 An fMRI investigation of procedural learning in unaffected siblings of individuals ROI-based analyses with schizophrenia Xi, Y. B. 2016 Anterior Cingulate Cortico-Hippocampal Dysconnectivity in Unaffected Relatives of Only functional connectivity analysis Schizophrenia Patients: A Stochastic Dynamic Causal Modeling Study Xiao, B. 2017 Abnormalities of localized connectivity in schizophrenia patients and their Resting-state study unaffected relatives: a meta-analysis of resting-state functional magnetic resonance imaging studies Yu, Y. 2013 Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern Resting-state study analysis of resting-state fMRI from schizophrenic patients and their healthy siblings Zhang, R. 2016 Working Memory in Unaffected Relatives of Patients With Schizophrenia: A Meta-Analysis Not peer-reviewed original article of Functional Magnetic Resonance Imaging Studies Mitchell, R. L. C. 2001 fMRI and cognitive dysfunction in schizophrenia Not peer-reviewed original article Ceaser, A. 2013 COMT influences on prefrontal and striatal blood oxygenation level-dependent responses Coordinates or Z/T-statistics not during working memory among individuals with schizophrenia, their siblings, and healthy available controls Diwadkar, V. A. 2011 Fronto-parietal hypo-activation during working memory independent of structural ROI-based analyses abnormalities: conjoint fMRI and sMRI analyses in adolescent offspring of schizophrenia patients Diwadkar, V. A. 2011 Hypo-activation in the executive core of the sustained attention network in ROI-based analyses adolescent offspring of schizophrenia patients mediated by premorbid functional deficits Diwadkar, V. A. 2012 The neural correlates of performance in adolescents at risk for schizophrenia: inefficiently Coordinates or Z/T-statistics not increased cortico-striatal responses measured with fMRI available Habel, U. 2002 Neuronal substratum for emotional impairments in patients with schizophrenia - results of Not peer-reviewed original article neuroimaging Bakshi, N. 2011 Inefficiently increased anterior cingulate modulation of cortical systems during working ROI-based analyses memory in young offspring of schizophrenia patients Dauvermann, M. R. 2012 Relationship Between Gyrification and Functional Connectivity of the Prefrontal Cortex in ROI-based analyses Subjects at High Genetic Risk of Schizophrenia Diwadkar, V. A. 2014 Dysfunction and Dysconnection in Cortical-Striatal Networks during Sustained Only functional connectivity analysis Attention: Genetic Risk for Schizophrenia or Bipolar Disorder and its Impact on Brain Network Function Jamadar, S. D. 2013 Semantic association fMRI impairments represent a potential schizophrenia biomarker High-risk individuals and healthy controls not compared

48 Whalley, H. C. 2008 Hypofrontality in subjects at high genetic risk of schizophrenia with depressive symptoms High-risk individuals and healthy controls not compared Wolf, D. H. 2011 Amygdala abnormalities in first-degree relatives of individuals with schizophrenia unmasked Coordinates or Z/T-statistics not by benzodiazepine challenge available

49 Supplementary Table 3. The VBM studies that were identified in literature search on the basis of title and abstract and that were excluded in the later phase.

First author Published Title Reason for exclusion Bois, C. 2015 Cortical Surface Area Differentiates Familial High Risk Individuals Who Go on to Not VBM study Develop Schizophrenia Borgwardt, S.J. 2010 Regional gray matter volume in monozygotic twins concordant and discordant for High-risk group size < 10 schizophrenia Cannon, T.D. 1998 Regional gray matter, white matter, and cerebrospinal fluid distributions in schizophrenic Not VBM study patients, their siblings, and controls Cannon, T.D. 2002 Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray- Not VBM study matter deficits in twins discordant for schizophrenia Cannon, T.D. 2005 Association of DISC1/TRAX haplotypes with schizophrenia, reduced prefrontal gray High-risk individuals and healthy matter, and impaired short- and long-term memory controls not compared Cao, H. 2016 Altered Functional Subnetwork During Emotional Face Processing: A Potential Not VBM study Intermediate Phenotype for Schizophrenia Chang, M. 2016 Voxel-Based Morphometry in Individuals at Genetic High Risk for Schizophrenia and Statistical test/reporting of findings not Patients with Schizophrenia during Their First Episode of Psychosis applicable Chen, Y.H. 2018 Associations and Heritability of Auditory Encoding, Gray Matter, and Attention in Not VBM study Schizophrenia Deng, W. 2006 Voxel-based morphometric analysis of gray matter in first-episode schizophrenia and Statistical test/reporting of findings not their unaffected relatives: A 3T MR investigation applicable Diwadkar, V.A. 2006 Genetically predisposed offspring with schizotypal features: an ultra high-risk group for High-risk individuals and healthy schizophrenia? controls not compared Diwadkar, V.A. 2011 Fronto-parietal hypo-activation during working memory independent of structural Not VBM study abnormalities: conjoint fMRI and sMRI analyses in adolescent offspring of schizophrenia patients Fan, Y. 2008 Unaffected family members and schizophrenia patients share brain structure patterns: A Statistical test/reporting of findings not high-dimensional pattern classification study applicable Goghari, V.M. 2014 Relationship between prefrontal gray matter volumes and working memory performance Not VBM study in schizophrenia: a family study Gogtay, N. 2007 Cortical brain development in nonpsychotic siblings of patients with childhood-onset Not VBM study schizophrenia

50 Goldman, A.L. 2008 Heritability of brain morphology related to schizophrenia: a large-scale automated Not VBM study magnetic resonance imaging segmentation study Gong, Q. 2018 A transdiagnostic neuroanatomical signature of psychiatric illness Not VBM study Ho, B.C. 2007 MRI brain volume abnormalities in young, nonpsychotic relatives of schizophrenia Not VBM study probands are associated with subsequent prodromal symptoms Hu, M. 2013 Decreased left middle temporal gyrus volume in antipsychotic drug-naive, first-episode Statistical test/reporting of findings not schizophrenia patients and their healthy unaffected siblings applicable Huang, L. 2016 The impact of CACNA1C allelic variation on regional gray matter volume in Chinese High-risk individuals and healthy population controls not compared Ivleva, E.I. 2013 Gray matter volume as an intermediate phenotype for psychosis: Bipolar-Schizophrenia High-risk individuals and healthy Network on Intermediate Phenotypes (B-SNIP) controls not compared Ivleva, E.I. 2017 Brain Structure Biomarkers in the Psychosis Biotypes: Findings From the Bipolar- High-risk individuals and healthy Schizophrenia Network for Intermediate Phenotypes controls not compared Knöchel, C. 2016 Shared and distinct gray matter abnormalities in schizophrenia, schizophrenia relatives Not VBM study and bipolar disorder in association with cognitive impairment Lee, P.H. 2013 Neural correlate of impulsivity in subjects at ultra-high risk for psychosis High-risk individuals and healthy Lee, T.Y. 2016 Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and Highcontrols-risk not individuals compared and healthy schizophrenia controls not compared Lui, S. 2009 Neuroanatomical differences between familial and sporadic schizophrenia and their Statistical test/reporting of findings not parents: An optimized voxel-based morphometry study applicable Lymer, G.K.S. 2006 Brain - behaviour relationships in people at high genetic risk of schizophrenia Not VBM study Marcelis, M. 2003 Searching for a structural endophenotype in psychosis using computational morphometry Statistical test/reporting of findings not applicable Mattai, A.A. 2011 Normalization of cortical gray matter deficits in nonpsychotic siblings of patients with Not VBM study childhood-onset schizophrenia McDonald, C. 2004 Association of genetic risks for schizophrenia and bipolar disorder with specific and High-risk individuals and healthy generic brain structural endophenotypes controls not compared Nenadic, I. 2015 Brain structure in people at ultra-high risk of psychosis, patients with first-episode Statistical test/reporting of findings not schizophrenia, and healthy controls: a VBM study applicable Oertel-Knöchel, V. 2012 Cortical-basal ganglia imbalance in schizophrenia patients and unaffected first-degree High-risk individuals and healthy relatives controls not compared Orešič, M. 2012 Phospholipids and insulin resistance in psychosis: a lipidomics study of twin pairs Not VBM study discordant for schizophrenia

51 Pietiläinen, O.P. 2009 Association of AKT1 with verbal learning, verbal memory, and regional cortical gray High-risk individuals and healthy matter density in twins controls not compared Pol, H.E.H. 2004 Gray and white matter density abnormalities in monozygotic and same-sex dizygotic Statistical test/reporting of findings not twins discordant for schizophrenia using voxel-based morphometry applicable Raznahan, A. 2011 Catechol-o-methyl transferase (COMT) val158met polymorphism and adolescent cortical Not VBM study development in patients with childhood-onset schizophrenia, their non-psychotic siblings, and healthy controls Schneider-Axmann, T. 2006 Relation between cerebrospinal fluid, gray matter and white matter changes in families Not VBM study with schizophrenia Shi, F. 2012 Altered structural connectivity in neonates at genetic risk for schizophrenia: a combined Not VBM study study using morphological and white matter networks Soh, P. 2015 Joint Coupling of Awake EEG Frequency Activity and MRI Gray Matter Volumes in the Not VBM study Psychosis Dimension: A BSNIP Study Sprooten, E. 2013 Cortical thickness in first-episode schizophrenia patients and individuals at high familial Not VBM study risk: A cross-sectional comparison Solé-Padullés, C. 2016 Altered Cortico-Striatal Connectivity in Offspring of Schizophrenia Patients Relative to Not VBM study Offspring of Bipolar Patients and Controls Sugranyes, G. 2015 Gray Matter Volume Decrease Distinguishes Schizophrenia From Bipolar Offspring Statistical test/reporting of findings not During Childhood and Adolescence applicable Sugranyes, G. 2017 Clinical, Cognitive, and Neuroimaging Evidence of a Neurodevelopmental Continuum in Not VBM study Offspring of Probands With Schizophrenia and Bipolar Disorder Tijms, B.M. 2015 Grey matter networks in people at increased familial risk for schizophrenia Not VBM study Turner, J.A. 2012 Heritability of multivariate gray matter measures in schizophrenia Not VBM study Van der Auwera, S. 2017 Predicting brain structure in population-based samples with biologically informed genetic High-risk individuals and healthy scores for schizophrenia controls not compared van Haren, N.E. 2012 The genetic and environmental determinants of the association between brain Not VBM study abnormalities and schizophrenia: the schizophrenia twins and relatives consortium Watsky, R.E. 2016 Severity of Cortical Thinning Correlates With Schizophrenia Spectrum Symptoms Not VBM study Wei, Q. 2012 The effect of DISC1 on regional gray matter density of schizophrenia in Han Chinese High-risk individuals and healthy population controls not compared Wright, C. 2016 Polymorphisms in MIR137HG and microRNA-137-regulated genes influence gray matter High-risk individuals and healthy structure in schizophrenia controls not compared Yang, Y. 2010 The contributions of disease and genetic factors towards regional cortical thinning in Not VBM study schizophrenia: the UCLA family study

52 Supplementary Table 4. Data from the BrainMap database (http://www.brainmap.org/taxonomy/behaviors.html).

Behavioral domain N of publications Description Action: Execution 309 The state or process of executing an overt movement of the body (other than speech). Action: Execution, Speech 93 The state or process of overtly speaking. Action: Imagination 50 The state or process of imagining an overt movement of the body. Action: Inhibition 148 The state or process of inhibiting an overt movement of the body. Action: Motor Learning 26 The state or process of learning how to execute an overt movement of the body. Action: Observation 45 The state or process of observing an overt movement of the body. Action: Preparation 21 The state or process of preparing for an overt movement of the body. Action: Rest 76 The state or process of resting from overt movements of the body. Cognition: Attention 646 The act or state of attending by applying the mind to any object of sense or thought. Cognition: Language 43 The mental faculty associated with knowledge of a system of objects or symbols, such as sounds or character sequences, that can be combined in various ways following a set of rules, especially to communicate thoughts, feelings, or instructions. Cognition: Language, Orthography 84 The mental faculty associated with the part of language study concerned with letters and spelling. Cognition: Language, Phonology 91 The mental faculty associated with knowledge of the distribution and patterning of speech sounds in a language and of the tacit rules governing pronunciation. Cognition: Language, Semantics 311 The mental faculty associated with knowledge of meaning in language forms. Cognition: Language, Speech 230 The mental faculty associated with knowledge of overtly or covertly speaking. Cognition: Language, Syntax 38 The mental faculty associated with knowledge of the rules for the formation of grammatical sentences in a language. Cognition: Memory, Other 24 The mental faculty of retaining and reviving facts, events, or impressions, or of recalling or recognizing previous experiences. Cognition: Memory.Explicit 260 The memory that consists of information stored and retrieved explicitly from the external world. Cognition: Memory, Working 291 TheThis memoryinformation for intermediateis about a specific results event that mustthat hasbe heldoccurred during at thinking.a specific time and place. Cognition: Music 34 TheAssociations mental faculty are done associated with previously with the relatedart of sound stimuli in ortime experiences that expresses in the ideas formation, and em storageotions inand significantsubsequent formsretrieval through of these the memories.elements of rhythm, melody, harmony, and color. Cognition: Reasoning 264 The mental faculty of forming conclusions, judgments, or inferences from facts or premises. Cognition: Social Cognition 122 The mental faculty associated with how people process social information, especially its encoding, storage, retrieval, and application to social situations. Cognition: Somatic 22 The mental faculty associated with knowledge of one's body. Cognition: Spatial 62 The mental faculty associated with awareness of the three-dimensional expanse in which all material objects are located and all events occur.

53 Cognition: Temporal 30 The mental faculty associated with the system of sequential relations that any event has to any other as past, present, or future. Emotion: Negative 37 The experience of negative emotion subsumes a variety of emotions including anger, contempt, disgust, guilt, fear, anxiety, hate, etc. Emotion: Negative, Anger 41 An emotion of wrath or ire characterized by displeasure and belligerence aroused by a wrong. Emotion: Negative, Anxiety 52 An emotion characterized by distress or uneasiness of mind caused by fear of danger or misfortune. Emotion: Negative, Disgust 50 An emotion characterized by a strong distaste, nausea, or loathing. Emotion: Negative, Fear 103 An emotion of being afraid aroused by distress, impending danger, evil, pain, etc. Emotion: Negative, Sadness 72 An emotion of sorrow or mourning characterized by unhappiness or grief. Emotion: Positive 21 The experience of positive emotion subsumes a variety of emotions including joy, happiness, contentment, love, etc. Emotion: Positive, Happiness 88 An emotion of well-being ranging from contentment to intense joy (excluding humor). Emotion: Valence 24 The intrinsic attractiveness/"good"-ness (positive valence) or averseness/"bad"-ness (negative valence) of an event, object, or situation. Emotion 219 Any affective processes that qualify as Emotion, but do not fit into any of the other Emotion sub- domains. Interoception: Gastrointestinal-Genitourinary 17 Awareness of pressure or distension in gastrointestinal or genitourinary systems, including the mouth, esophagus, stomach, bladder, anus, rectum. Interoception: Hunger 17 The need for food. Interoception: Respiration Regulation 21 The need for respiration. Interoception: Sexuality 50 The need for sexual activity. Perception: Audition 155 The sense of hearing. Perception: Gustation 45 The sense of tasting. Perception: Olfaction 42 The sense of smelling. Perception: Somesthesis 109 The sensory systems associated with the skin, including touch, pressure, temperature and position. Perception: Somesthesis, Pain 117 The senses of bodily perception associated with an unpleasant sensation occurring in varying degrees of severity as a consequence of injury, disease, or emotional disorder. Perception: Vision 170 The sense of sight. Perception: Vision, Color 35 The visual perception of the quality of an object or substance with respect to light reflected by the object, usually determined visually by measurement of hue, saturation, and brightness of the reflected light.

54 Perception: Vision, Motion 92 The visual perception of the action or process of moving or of changing place or position. Perception: Vision, Shape 158 The visual perception of the quality of a distinct object in having an external surface or outline of specific form or figure.

55 Supplementary Table 5. The results of the meta-analyses on the fMRI (28 datasets) and VBM (9 datasets) studies that included only first-degree relatives of schizophrenia patients.

Coordinate Test statistic p Voxels Description s (MNI) of SDM fMRI studies FRs > Controls 46, 6, 34 2.078 0.000007 418 Right precentral gyrus, BA 44

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